用自监督学习和静止卫星数据实时追踪野火蔓延与烟雾扩散。
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
- 基于自监督深度学习，融合GOES-18与TEMPO辐射率数据。
- 实现小时级野火前沿与烟雾羽流的精准分割，优于现有业务产品。
- 适合气象、环境监测及灾害应急响应人员使用。
本研究展示利用美国宇航局TEMPO卫星任务提供的每小时高分辨率数据，结合自监督深度学习技术，显著提升美国西部野火及空气质量监测能力。通过创新的自监督深度学习系统，成功区分烟雾羽流与云层，利用GOES-18与TEMPO数据实现了小时级野火前沿与烟雾扩散的近实时映射。不同传感模态生成的烟雾与火线掩码具有高度一致性，且在相同案例中相较现有业务产品有显著改进。
原文摘要 · Abstract (English)
This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMPO satellite mission and advances in self-supervised deep learning. Here we demonstrate the efficacy of deep learning for mapping the near real-time hourly spread of wildfire fronts and smoke plumes using an innovative self-supervised deep learning-system: successfully distinguishing smoke plumes from clouds using GOES-18 and TEMPO data, strong agreement across the smoke and fire masks generated from different sensing modalities as well as significant improvement over operational products for the same cases.
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